CN108871427B - A kind of water quality detection method of water source - Google Patents

Abstract

The invention provides a water quality detection method for a water source, including the following processes: arranging a plurality of turbidity sensors underwater in the water source, converting the values measured by the turbidity sensors into a turbidity matrix; The gray correlation degree fusion weight of the sensor value is calculated; the similarity fusion weight of the water source environment turbidity sensor value based on the similarity is calculated; the combination weight based on the principle of minimum relative information entropy is calculated; the water source water quality fusion model is obtained according to the combination weight.

Description

A kind of water quality detection method of water source

technical field

The invention relates to a water quality monitoring method of a water source, in particular to a water quality monitoring method of a water source.

Background technique

my country has begun to test the environmental parameters of drinking water sources since the 1980s and 1990s, and the development of intelligent monitoring systems for environmental parameters of drinking water sources and the test application in the intelligent control system has never been interrupted. The level of intelligence and science and technology is relatively low, and it cannot monitor the same water source at multiple points, and cannot accurately control the turbidity of drinking water sources. According to the current situation, a drinking water source monitoring device is designed to Multi-point monitoring of various parameters of the water environment factors of drinking water sources, using the multi-point turbidity fusion model of the water source environment to accurately fuse the turbidity values of the multi-point detection of the water source environment to improve the accuracy of turbidity detection in the water source environment, Robustness and reliability, but also high versatility and practicality.

SUMMARY OF THE INVENTION

The invention provides a water quality detection method for a water source, and the method has high parameter precision for monitoring the water factor of the water environment.

The technical scheme for realizing the purpose of the present invention is: a water quality detection method for a water source, which is characterized in that it includes the following process:

Step 1: Arrange several turbidity sensors underwater at the water source, and convert the values measured by the turbidity sensors into a turbidity matrix;

Step 2: Calculate the gray correlation degree fusion weight of the water source environmental turbidity sensor value based on the gray correlation degree;

Step 3, calculating the similarity fusion weight of the water source environmental turbidity sensor value based on the similarity;

Step 4, calculate the weight of the combination based on the principle of minimum relative information entropy;

Step 5: Obtain a water source water quality fusion model according to the combined weights.

Compared with the prior art, the present invention has the following advantages: (1) It solves the problem that the existing monitoring equipment cannot monitor the same water source at multiple points and cannot accurately control the turbidity of the drinking water source. The turbidity values of environmental multi-point detection are accurately fused to improve the accuracy, robustness and reliability of turbidity detection in the water source environment. It also has high versatility and practicability. At the same time, the control and adjustment of highly intelligent and user-friendly detection The ability to better reduce the cost of monitoring, easy to use, safe and reliable; (2) Convert the turbidity parameter of the water factor in the water source environment into the form of interval numbers, define the similarity between the two interval numbers, and build a similarity matrix, The mobile car equipped with turbidity sensor moves every 30° to detect the turbidity in the water source environment. The similarity fusion weight α _i of the turbidity sensor value at the detection point improves the accuracy and scientificity of the turbidity fusion value of the water source environment.

The present invention will be further described below with reference to the accompanying drawings.

Description of drawings

Fig. 1 is the schematic flow chart of the method of the present invention.

Figure 2 is a schematic structural diagram of a lifting rail device in a drinking water source monitoring device.

Figure 3 is a schematic diagram of the structure of the activated carbon injection device in the present invention.

Figure 4 is a schematic diagram of the structure of the annular track in the present invention.

Fig. 5 is the internal structure diagram of the base in the present invention.

Fig. 6 is the position distribution diagram of screw and polished rod in the present invention.

Figure 7 is a schematic diagram of the base in the present invention.

Fig. 8 is a partial enlarged view of the mobile trolley in the present invention.

Detailed ways

With reference to Fig. 1, a kind of water quality detection method of water source is characterized in that, comprises the following process:

Step 1: Arrange several turbidity sensors underwater at the water source, and convert the values measured by the turbidity sensors into a turbidity matrix;

Step 2: Calculate the gray correlation degree fusion weight of the water source environmental turbidity sensor value based on the gray correlation degree;

Step 3, calculating the similarity fusion weight of the water source environmental turbidity sensor value based on the similarity;

Step 4, calculate the weight of the combination based on the principle of minimum relative information entropy;

Step 5: Obtain a water source water quality fusion model according to the combined weights.

The turbidity matrix in step 1 is

Among them, m is the number of sensors, and n is the number of sensor measurement periods.

The specific process of step 2 is as follows:

Step 2.1, according to formula (2), calculate the correlation degree ζ _ij between each sensor and the maximum temperature value of m sensors in each K period, k is 1, 2, ... n,

in,

Step 2.2, construct the gray correlation matrix B of the water source environmental turbidity sensor,

Step 2.3, according to formula (4), calculate the average correlation degree ζ _i between the turbidity value detected by each sensor and the maximum turbidity value

Step 2.4, according to formula (5), calculate the correlation degree λ _ij between each sensor and the minimum turbidity value of m sensors in each K period

Step 2.5, construct the correlation matrix C

Step 2.6, according to formula (7), calculate the average correlation degree η _i between the turbidity value detected by each sensor and the minimum turbidity value

Step 2.7, according to formula (8), obtain the gray correlation degree fusion weight β _i of the water source environmental turbidity sensor value

The specific process of step 3 is as follows:

Step 3.1, according to the similarity between any two sensors detecting the turbidity of the water source environment at the same time period, construct a similarity matrix S for the sensor to detect the turbidity of the water source

S_ab表示a和b的相似度，a＝[a^L,a^U]，b＝[b^L,b^U]，q_j，j＝1,2,3,4分别为a^L、a^U、b^L、b^U中的第j大的数，in,

S _ab represents the similarity between a and b, a=[a ^L , a ^U ], b=[b ^L , b ^U ], q _j , j=1, 2, 3, 4 are respectively a ^L , a ^U , The jth largest number in b ^L and b ^U ,

Step 3.2, calculate the average similarity Si of each sensor in each row of matrix _S

Step 3.3, calculate the similarity fusion weight α _i of the water source environmental turbidity sensor value

The weight w _i of the combination based on the principle of minimum relative information entropy in step 4

The model in step 5 is

Among them, i is the index value of the detection point, and x _i is the temperature of the ith detection point at time k.

2 to 8 , a drinking water source monitoring device implementing the above detection method includes an activated carbon injection device 2 , a lifting rail device 1 , a water source detection device 21 and a multi-point turbidity fusion model of the water source environment.

3 to 6 , the lifting track device includes a base 7 , a lower bottom plate 8 , an annular track 5 , and a lifting device connecting the base 7 and the annular track 5 . The lifting device includes a stepping motor 19, two lead screws 18, a polished rod 17, three pulleys 15, a synchronous belt 16, two supporting feet 6 with internal threads, and one supporting foot 9 with a smooth hole. Two supporting feet 6 with internal threads and one supporting foot 9 with a smooth hole are distributed on the bottom surface of the annular track 5 at 120 degrees. The feet 6 and a supporting foot 9 with a light hole are matched, the lower ends of two lead screws 18 and one smooth rod 17 pass through the upper surface of the base 7 and are respectively fixedly connected to the three pulleys 15, and the lower ends of the two lead screws 18 are arranged at On the lower base plate 8 , the lower end of the polished rod 17 is connected with the drive shaft of the stepping motor 19 arranged on the lower base plate 8 , and the three pulleys 15 are connected by a synchronous belt 16 .

Referring to FIGS. 2 and 7 , the activated carbon injection device 2 includes a moving cart 22 , a housing body, a crank-slider mechanism, and a cuboid 13 without a cover. The mobile trolley 22 is arranged on the annular track 5 and moves around the annular track 5, the casing body is arranged on the mobile trolley 22, the casing body is provided with an inner cavity, the side wall of the casing body is provided with a throwing port communicating with the inner cavity, and an electromagnetic field is arranged on the throwing port. The valve 4 and the crank-slider mechanism are arranged in the inner cavity of the housing body, and the coverless cuboid 13 carries the activated carbon, and is driven by the crank-slider mechanism to extend or lock the injection port. The crank-slider mechanism includes a frame 10 , a crank 11 , a connecting rod 12 , and a slider 23 . The end of the crank 11 is fixed on the bottom surface of the inner cavity of the shell body, the end of the connecting rod 12 is rotatably connected with the front end of the crank 11, the end of the frame 10 is slidably connected with the crank 11, the slider 23 is rotatably connected with the front end of the connecting rod 12 and is fixedly connected with the front end of the frame 10 , the slider 23 is fixedly connected with the coverless cuboid 13 .

Specifically, the end surface of the frame 10 is provided with a square hole, and the crank 11 passes through the square hole.

The water source detection device includes a turbidity sensor, a temperature sensor, a TDS sensor, and a PH value sensor. There are four turbidity sensors, three of which are evenly distributed on the base 7 at points A, B, and C in FIG. 1 , and the other one is arranged on the outer wall of the housing body.

The multi-point turbidity fusion model of the water source environment converts the multi-point turbidity values detected by the water source detection device in the water source environment into interval values, defines the similarity and gray correlation of the interval values of the turbidity sensor, and constructs the similarity matrix and Gray correlation matrix, the similarity fusion weight of the turbidity sensor interval number of each detection point in the water source environment, the average correlation product of the turbidity sensor interval value of each detection point and the maximum and minimum interval values of the water source environment turbidity sensor The ratio of the reciprocal to the reciprocal sum of the average correlation product of the turbidity sensor interval value of the entire water source environment detection point and the maximum and minimum interval values is the gray correlation degree fusion weight of the turbidity sensor value of the detection point. The turbidity of each detection point The ratio of the root mean square of the similarity fusion weight of the sensor value fusion and the gray correlation fusion weight product to the sum of the root mean square of the similarity fusion weight of the sensor value and the grey correlation fusion weight product of the turbidity sensor value in the entire water source is the turbidity of the detection point. The combined weight of the fusion of the turbidity sensor values, the sum of the combined weight products of the turbidity sensor values of each detection point in the water source environment and the fusion of the respective turbidity sensor values is the value of the turbidity fusion model of multiple detection points in the water source environment.

The lifting track device is placed in the water source, and the annular track 5 is driven up and down by the screw drive mechanism. The sensors in the water source detection device 5 distributed on the side of the mobile trolley detect water quality parameters respectively, and the collected water quality parameters The information passes through. The wireless module communicates with the user, so that the user can monitor the water quality of the water source in real time. At the same time, using the turbidity fusion model of multiple detection points in the water source environment, any two sensors can detect the similarity of the turbidity of the water source environment at the same time period, and construct a similarity matrix S for the sensor to detect the turbidity of the water source, which can control the turbidity more accurately. The turbidity of the water source environment of the entire water source area, if the turbidity of the water source area exceeds the preset value, the electromagnetic valve 4 is opened, and the coverless cuboid 13 filled with solid activated carbon passes through the electromagnetic valve 4 through the action of the crank-slider mechanism. Go to the water surface for water purification.

Working principle: The lifting track device is placed in the water source, and the lifting motion of the annular track 5 is controlled by the cooperation of the screw transmission mechanism and the polished rod guide rail. The sensors in the water source detection device distributed on the side 25 of the mobile trolley detect water quality parameters respectively, and follow the steps. With the lifting motion of the circular track, the water source detection device can detect the water quality of the water source at different depths. The mobile car moves on the circular track to detect the water quality of the water source at multiple points on the same level. The collected water quality parameters are transmitted through wireless The module communicates with the user, and uses the turbidity fusion model of multiple detection points in the water source environment to detect the similarity of the turbidity of the water source environment by any two different sensors at the same time period, and construct the similarity matrix S for the sensors to detect the turbidity of the water source, which can be more accurate. Control the turbidity of the water source environment of the entire water source, so that users can accurately monitor the water environment factors of the water source. If the turbidity of the water source exceeds the preset value, the electromagnetic valve is opened, and the coverless cuboid filled with solid activated carbon is extended to the water surface through the electromagnetic valve through the action of the crank-slider mechanism for water purification.

Claims (6)

1. a method for detecting water quality in a water source, is characterized in that, comprises the following process: Step 1: Arrange several turbidity sensors underwater at the water source, and convert the values measured by the turbidity sensors into a turbidity matrix; Step 2: Calculate the gray correlation degree fusion weight of the water source environmental turbidity sensor value based on the gray correlation degree; Step 3, calculating the similarity fusion weight of the water source environmental turbidity sensor value based on the similarity; Step 4, calculate the weight of the combination based on the principle of minimum relative information entropy; Step 5, obtain the water source water quality fusion model according to the combined weight; A drinking water source monitoring device for realizing the above detection method, comprising an activated carbon feeding device (2), a lifting rail device (1), and a water source detection device (21); The activated carbon feeding device (2) includes a moving trolley (22), a housing body, a crank-slider mechanism, and a coverless cuboid (13); the moving trolley (22) is arranged on the annular track (5) and moves around the annular track (5), The housing body is arranged on the moving trolley (22), the housing body is provided with an inner cavity, the side wall of the housing body is provided with a throwing port which is communicated with the inner cavity, an electromagnetic valve (4) is arranged on the throwing port, and the crank-slider mechanism is arranged on the side of the casing body. In the inner cavity, the coverless cuboid (13) carries the activated carbon, and is driven by a crank-slider mechanism to extend or lock the injection port; the crank-slider mechanism includes a frame (10), a crank (11), a connecting rod (12), The slider (23); the end of the crank (11) is fixed on the bottom surface of the inner cavity of the housing body, the end of the connecting rod (12) is rotatably connected with the front end of the crank (11), the end of the frame (10) is slidably connected with the crank (11), and the slider (23) rotatably connected with the front end of the connecting rod (12) and fixedly connected with the front end of the frame (10), and the slider (23) is fixedly connected with the coverless cuboid (13); The lifting track device includes a base (7), a lower bottom plate (8), a circular track (5), and a lifting device connecting the base (7) and the annular track (5); the lifting device includes a stepping motor (19), two lead screws (18), a polished rod (17), three pulleys (15), a timing belt (16), two supporting feet (6) with internal threads, and one supporting foot (9) with a smooth hole; two A support foot (6) with an internal thread and a support foot (9) with a smooth hole are distributed at 120 degrees on the bottom surface of the annular track (5), two lead screws (18) and a smooth rod (17) respectively Matching with two supporting feet (6) with internal threads and one supporting foot (9) with smooth holes, the lower ends of two lead screws (18) and one smooth rod (17) pass through the upper surface of the base (7) respectively. It is fixedly connected with the three pulleys (15), the lower ends of the two lead screws (18) are arranged on the lower base plate (8) through bearings, and the lower ends of the polished rods (17) are connected to the stepping motor (19) arranged on the lower base plate (8). ) is connected with the drive shaft, and the three pulleys (15) are connected by a synchronous belt (16); The water source detection device (21) includes a turbidity sensor, a temperature sensor, a TDS sensor, and a pH value sensor; four turbidity sensors are arranged, three of which are evenly distributed on the base (7) and the other is arranged on the outer wall of the shell body . 2. The method according to claim 1, wherein the turbidity matrix in step 1 is

Among them, m is the number of sensors, and n is the number of sensor measurement periods. 3. method according to claim 2, is characterized in that, the concrete process of step 2 is: Step 2.1, according to formula (2), calculate the correlation degree ζ _ij between each sensor and the maximum temperature value of m sensors in each K period, k is 1, 2, ... n,

Step 2.2, construct the gray correlation matrix B of the water source environmental turbidity sensor,

Step 2.3, according to formula (4), calculate the average correlation degree ζ _i between the turbidity value detected by each sensor and the maximum turbidity value

Step 2.4, according to formula (5), calculate the correlation degree λ _ij between each sensor and the minimum turbidity value of m sensors in each K period

Step 2.5, construct the correlation matrix C

Step 2.6, calculate the average correlation degree ηi between the turbidity value detected by each sensor and the minimum turbidity value according to formula (7)

Step 2.7, according to formula (8), obtain the gray correlation degree fusion weight β _i of the water source environmental turbidity sensor value

4. method according to claim 3, is characterized in that, the concrete process of step 3 is: Step 3.1, according to the similarity between any two sensors detecting the turbidity of the water source environment at the same time period, construct a similarity matrix S for the sensor to detect the turbidity of the water source

in,

S _ab represents the similarity between a and b, a=[a ^L , a ^U ], b=[b ^L , b ^U ], q _j , j=1, 2, 3, 4 are respectively a ^L , a ^U , The jth largest number in b ^L and b ^U , Step 3.2, calculate the average similarity Si of each sensor in each row of matrix S

Step 3.3, calculate the similarity fusion weight αi of the water source environmental turbidity sensor value

5. method according to claim 4 is characterized in that, in step 4, the weight w _i of the combination based on the principle of minimum relative information entropy

6. The method according to claim 5, wherein the model in step 5 is

Among them, i is the index value of the detection point, and x _i is the temperature of the ith detection point at time k.

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